Automated visual inspection: a survey

This paper surveys publications, reports, and articles dealing with automated visual inspection for industry. The references are organized according to their contents: overview and discussions, rationales, components and design considerations, commercially available systems, applications. A number of applications and their inspection methodologies are discussed in detail: the inspection of printed circuit boards, photomasks, integrated circuit chips. Other inspection applications are listed as a bibliography. A list of selectively annotated references in commercially available visual inspection tools is also included.     

Automated visual inspection: 1981 to 1987

This bibliography lists journal publications, conference papers, research technical reports, and articles from trade journals on automated visual inspection for industry which were published during the years from 1981 to 1987. More than 600 references are included. References are organized into 13 categories according to subject matter. The categories are (1) books, (2) conferences and workshops, (3) general discussions and surveys, (4) inspection of printed circuit patterns, (5) inspection of solder joints, (6) inspection of microcircuit photomasks, (7) inspection of integrated circuits and hybrids, (8) inspection of other electrical and electronics components, (9) surface inspection, (10) X-ray inspection, (11) other inspection applications, (12) system components, and (13) inspection algorithms. References listed in each category are arranged in chronological order. The purpose is primarily to provide a complete bibliography for those interested in automated visual inspection. Some general observations have been made for the above areas of activity summarizing the advances that have taken place and the problems that remain to be solved.     

Automated visual inspection in the semiconductor industry: A survey

Automated visual inspection is an image-processing technique for quality control and production line automation. This paper reviews various optical inspection approaches in the semiconductor industry and categorize the previous literatures by the inspection algorithm and inspected products. The vision-based algorithms that had been adopted in the visual inspection systems include projection methods, filtering-based approaches, learning-based approaches, and hybrid methods. To discuss about the practical applications, the semiconductor industry covers the manufacturing and production of wafer, thin-film transistor liquid crystal displays, and light-emitting diodes. To improve the yield rate and reduce manufacturing costs, the inspection devices are widely installed in the design, layout, fabrication, assembly, and testing processes of production lines. To achieve a high robustness and computational efficiency of automated visual inspection, interdisciplinary knowledge between precision manufacturing and advanced image-processing techniques is required in the novel system design. This paper reviews multiple defect types of various inspected products which can be referenced for further implementations and improvements.     

Automated visual inspection of rolled metal surfaces

A prototype for an automated visual on-line metal strip inspection system is described. The system is capable of both detecting and classifying surface defects in copper alloy strips, and it has been installed for evaluation in a production line in a rolling mill. The image acquisition part of the system is based on a CCD line scan camera and condensing bright field illuminators. The inspection algorithms are based on morphological preprocessing and combined statistical and structural defect recognition. The image processing hardware consists of commercial modules. An analysis of this implementation is presented. A similar inspection principle has also been successfully applied to steel strip inspection.     

Automated visual inspection of imprint quality of pharmaceutical tablets

Visual appearance is an important quality factor of pharmaceutical tablets. Moreover, it plays a key role in identification of tablets, which is needed to prevent mix-ups among various types of tablets. Since identification of tablets is most frequently done by imprints, good imprint quality, a property that makes the imprint readable, is of utmost importance in preventing mix-ups among the tablets. In this paper, we propose a novel method for automated visual inspection of tablets. Besides defect detection, imprint quality inspection is also considered. Performance of the method was evaluated on three different real tablet image databases of imprinted tablets. A “gold standard” was established by manually classifying tablets into a good and a defective class. The receiver operating characteristics (ROC) analysis indicated that the proposed method yields better sensitivity and specificity than the previous defect detection method.     

视觉测量中椭圆自动检测与定位方法

针对视觉测量中椭圆目标的检测效率与定位精度较低的问题,提出一种高效的椭圆识别与定位方法。该方法利用回光反射控制点的强反射特性,提取目标的边缘信息,对连续的边缘进行分组,利用椭圆长轴信息对每组边缘数据进行椭圆识别,采用基于变量含误差模型的椭圆拟合方法,实现椭圆目标中心的精确定位。实验证明,该方法具有快速、自动化、定位精度高、鲁棒性好等优点,在视觉测量中具有广泛的应用前景。     

Automated visual inspection of ripple defects using wavelet characteristic based multivariate statistical approach

This paper presents a wavelet characteristic based approach for the automated visual inspection of ripple defects in the surface barrier layer (SBL) chips of ceramic capacitors. Difficulties exist in automatically inspecting ripple defects because of their semi-opaque and unstructured appearances, the gradual changes of their intensity levels, and the low intensity contrast between their surfaces and the rough exterior of a SBL chip. To overcome these difficulties, we first utilize wavelet transform to decompose an image and use wavelet characteristics as texture features to describe surface texture properties. Then, we apply multivariate statistics of Hotelling T², Mahalanobis distance D², and Chi-square X², respectively, to integrate the multiple texture features and judge the existence of defects. Finally, we compare the defect detection performance of the three wavelet-based multivariate statistical models. Experimental results show that the proposed approach (Hotelling T²) achieves a 93.75% probability of accurately detecting the existence of ripple defects and an approximate 90% probability of correctly segmenting their regions.     

Automated capsule inspection method

This method is based on the measurement of quantities associated with features in the projection of a capsule under inspection, such as head roundness, joint collinearity, area and length of the projection, and on the comparison of them with those in a standard projection.

The apparatus is composed of two TV cameras associated with strobe lighting, logic circuits, two 8 bit microprocessors and mechanisms to feed, transport, turn over and eject the capsules. The processing speed is 16 capsules s⁻¹. The inspection capability is confirmed to be of the same or higher level than inspection by human eyes.     

Automated facility inspection using robotics and BIM: A knowledge-driven approach

Facility inspection is crucial for ensuring the performance of built assets. A traditional inspection, characterized by humans’ physical presence, is laborious, time-consuming, and becomes difficult to implement because of travel restrictions amid the pandemic. This laborious practice can potentially be automated by emerging smart technologies such as robotics and building information model (BIM). However, little has been known on how such automation can be achieved, concerning the knowledge-intensive nature of facility inspection. To fill the gap, this research aims to develop a knowledge-driven approach that can synergize knowledge of diverse sources (e.g., explicit knowledge from BIM, and tacit experience in human minds) to allow autonomous implementation of facility inspection by robotic agents. At the core the approach is an integrated scene-task-agent (iSTA) model that formalizes engineering priori in facility management and integrates the rich contextual information from BIM. Experiments demonstrated the applicability of the approach, which can endow robots with autonomy and knowledge to navigate the challenging built environments and deliver facility inspection outcomes. The iSTA model is publicized online, in hope of further extension by the research community and practical deployment to enable automated facility inspection using robotics and BIM.     

Automated inspection of circular parts

Automated vision inspection systems are the most versatile non-contact inspection systems. They are fast, compact, and remarkably accurate, thereby making 100% on-line inspection possible in modern manufacturing systems.

Roundness in one of the basic geometric elements in engineering design and manufacturing. As a result its inspection is an important part of the inspection of many industrial parts.

This paper discusses least-squares and minimax mathematical models and algorithms to determine the out-of-roundness error in automated roundness inspection. These errors are measured with respect to an ideal circle of specified radius.     

Automated inspection of general shapes

The demand to minimize the number of defects along with the increasing availability of computerized vision systems has made the on-line inspection of all production parts a feasible option in modern manufacturing systems. Vision systems enable noncontact, and thus, nondestructive measurements. An image of the production part is electronically obtained and stored in digital form in a computer. In most cases, the image is then processed to identify the local edges of the object. At a higher image processing level, information on local edges is used to obtain the boundaries of the object. Measurements on the computationally obtained boundary can then be performed matheatically, allowing tests to verify the shape and dimensions of the production part. It is the purpose of this paper to investigate and present methods for the determination of shapes and the use of this information for on-line quality inspection.     

Automated inspection process planning: Algorithmic inspection feature recognition, and inspection case representation for CBR

General metrological inspection planning is among the least explored computer-aided process planning (CAPP) domains. This paper explores certain basic issues involved in inspection planning using case-based reasoning in an environment of a Generic CAPP Support System. Firstly, algorithmic methods for characterizing and extracting inspection features are proposed and discussed. A sequential knowledge based filtering method is developed to reduce the number of inspection features typically encountered in metrological inspection planning. Finally, a formalized approach for case representation of relevant inspection domain knowledge using a newly developed parametric-list technological feature graph (PLTFG) is presented.     

A rule based approach for visual pattern inspection

The authors investigate the use of a priori knowledge about a scene to coordinate and control bilevel image segmentation, interpretation, and shape inspection of different objects in the scene. The approach is composed of two main steps. The first step consists of proper segmentation and labeling of individual regions in the image for subsequent ease in interpretation. General as well as scene-specific knowledge is used to improve the segmentation and interpretation processes. Once every region in the image has been identified, the second step proceeds by testing different regions to ensure they meet the design requirements, which are formalized by a set of rules. Morphological techniques are used to extract certain features from the previously processed image for rule verification purposes. As a specific example, results for detecting defects in printed circuit boards are presented     

A software laboratory for visual inspection and recognition

We present the design criteria and the basic structure of GYPSY, a software laboratory for visual inspection and recognition in industrial automation tasks.GYPSY analyzes images of objects on the basis of their two-dimensional shape. It is fully portable and easily programmable. The user can write application programs in a Pascal-like language to give the system the ability to take over various tasks.GYPSY has been developed for a multi-microprocessor architecture designed in Italy especially for industrial automation.     

Automated X-ray inspection of aluminum castings

The experience gained with several approaches to automatic flaw detection in X-ray images of cast aluminum wheels is described. Basic problems are mentioned, and the applicability of segmentation methods to actual inspection tasks is demonstrated. The discussion focuses on the definition, extraction, and combination of local features for pixel classification. Results of pilot tests are described briefly. Further investigations are suggested, aiming at more generality of the methods and greater stability of the segmentation     

A simplified visual inspection system using a personal computer

For a practical visual inspection system, data processing in real time, flexibility for variety of products, and cost performance have been required. This article describes a simplified visual inspection system that has well-balanced performance that meets these requirements. Inspection data processing by software makes the inspection system flexible. Development of a simplified pattern matching method based on limiting the inspection within the area pointed by an user and direct memory access of binary image data performs a high speed inspection function. By using a personal computer as a processor of this system, a cost effective inspection system is realized.     

A multilevel information fusion approach for visual quality inspection

For visual quality inspection systems to be applicable in industrial settings, it is mandatory that they are highly flexible, robust and accurate. In order to improve these characteristics a multilevel information fusion approach is presented. A first fusion step at the feature-level enables the system to learn from an undefined number of potential defects which might be segmented from the images. This allows for the quality control operators to label the data at the image-level and the sub-image-level, and use this information during the learning process. Additionally, the operators are allowed to provide a confidence measure for their labelling. The additional information obtained from the increased flexibility of the operator inputs allows to build more accurate classifiers. A second fusion step at the decision-level combines the classifications of different classifiers, making the system more accurate and more robust with respect to the classification method chosen. The experimental results, using various artificial and real-world visual quality inspection data sets, show that each of these fusion approaches can significantly improve the classification accuracy. If both information fusion approaches are combined the accuracy increases even further, significantly outperforming each of the fusion approaches on their own.